The objective of the course is for the student to develop their knowledge and skills in Predictive Analytics. After passing the course, the student should be able to: 

1. Explain and use the concepts in predictive analytics
2. Describe the business situations where & how predictive analytics would, or should, be used.
3. Explain how predictive analytics is used to address organizational needs
4. Evaluate a predictive analytics technique
5. Analyze and reflect on the relationship between its components, current and future 
6. Plan & execute predictive analytics experiment

The Predictive Analytics course is aimed at providing knowledge to the students on how to make prediction using machine learning techniques. While scientists are accustomed to make predictions based on consolidated and accepted theories, nowadays big data analytics is able to deliver predictions based on executing a sequence of data processing steps. The course explains both the analytics process as well as the techniques for making predictions. The Predictive Analytics course, though, identify some of the key challenges faced, while making predictions. Techniques such as: naïve approach, moving averages, exponential smoothing, trend projection, regression, ANN and deep learning, will be studied in the course with supporting examples and use cases. 

Lectures, labs, assignments, case studies and/or project work. During the course, the students work with individual tasks and/or group work. Some assignments or case studies in the course might contain work in contact with or about the industry. The student uses different methods and techniques, and it is important to choose the right method, technique or computer support for each task. Before and after the tasks are solved, there are lectures to present and discuss different solutions.
Teaching is in English and on the Internet for distance students or on campus for students living here. IT support: Learning management system, e-mail and phone.  The learning management system is used for delivering course material, information and submissions. Knowledge is shared and created within the course through virtual meetings with teachers and other students for discussions, supervision, teamwork and seminars. For students on campus, there will be meetings on campus.  

Through written tests, individual and group/project assignment, different student abilities are examined. Those are: the ability to explain and use prediction techniques and the ability to solve business problems using prediction techniques individually and in groups. 

  Technical Requirements: access to computer with administrative rights, web camera, microphone and Internet connection.